Real-time Properties Research Articles

Tunable Au@SiO2/Au Film Metasurface as Surface Plasmon Resonance Enhancer for Direct and Ultrasensitive Detection of Exosomes.

Surface plasmon resonance (SPR) spectroscopy with non-labelling, sensitive, and real-time properties is critical for clinical diagnosis applications. However, conventional SPR sensors face the challenge of lower sensitivity and selectivity for trace exosomes assay in complex serum. We proposed a core-shell Au@SiO2-Au film (Au@SiO2-Au film) metasurface to enhance SPR signal based on systematic study on the relationship between gap modes and SPR enhancement. The self-assembled multifunctional peptide was designed as recognition layer with antifouling properties for ultrasensitive and selective detection of PD-L1+ exosomes in serum. The tuning electromagnetic (EM) field model by manipulating the gap was established to guide the preparation of Au@SiO2-Au film metasurface. The in-plane and out-of-plane coupling of Au@SiO2 nanoparticles (NPs) could greatly enlarge and enhance three-dimensional EM field to meet the size of exosomes located in the evanescent field. At the structural level, we achieved high sensitivity (0.16 particles/mL) and a broad response range (10-5 × 103 particles/mL) through optimizing the thickness of SiO2 and surface coverage of Au@SiO2. Furthermore, clinical sample assay achieved the optimal diagnostic accuracy (AUC = 0.97) for differentiating cancer patients from healthy controls. This work provides an opportunity for the construction of a tunable gap mode as SPR enhancer in a total internal reflection architecture. The systematic study on the relationship between gap modes and SPR sensitivity provides a broad scope for promoting direct, efficient, highly selective, and sensitive detection of SPR sensors for clinical application.

Towards in-line real-time characterization of roll-to-roll produced ZTO/Ag/ITO thin films by hyperspectral imaging

Large area manufacturing processes of thin films such as large-area vacuum roll-to-roll coating of dielectric and gas permeation barrier layers in industry require a precise control of e.g. film thickness, homogeneity, chemical compositions, crystallinity and surface roughness. In order to determine these properties in real time, hyperspectral imaging is a novel, cost-efficient, and fast tool as in-line technology for large-area quality control. We demonstrate the application of hyperspectral imaging to characterize the thickness of thin films of the multilayer system ZTO/Ag/ITO produced by roll-to-roll magnetron sputtering on 220 mm wide polyethylene terephthalate substrate. X-ray reflectivity measurements are used to determine the thickness gradients of roll-to-roll produced foils with sub nanometer accuracy that serve as ground truth data to train a machine learning model for the interpretation of the hyperspectral imaging spectra. Based on the model, the sub-layer thicknesses on the complete substrate foil area were predicted which demonstrates the capabilities of this approach for large-scale in-line real-time quality control for industrial applications.

Information-modeling system for prediction of the composition and properties of final slag in a blast furnace in real time

The article considers general characteristics of the algorithm for prediction of the composition of the final slag in a blast furnace in real time. This algorithm is based on fundamental knowledge on the processes occurring in the furnace and general laws of transient processes. It allows predicting at the current moment of time and for every hour ten hours ahead. A linearized model of the blast furnace process and a natural-mathematical approach are used. The model takes into account the dynamic characteristics of blast furnaces in various impact channels, which change and depend on the type of impact, operating parameters of the furnaces and properties of the melted raw material. This makes it possible to adjust the model to operating conditions of the object, to take into account changes in the composition and properties of iron ore and coke, blast and regime parameters of blast furnace smelting when modeling. The software of the information-modeling system for prediction of the composition and properties of the final slag in a blast furnace in real time was developed in the C# programming language based on the ASP.NET MVC framework using the .NET 5 cross-platform. The web application includes the following main functions: visualization of change APCS parameters and design parameters over time; slag mode diagnostics; modeling of transient processes of composition and properties of slag; prediction of slag composition and properties in real time and prediction history. The software architecture is described and its operation is illustrated. An assessment of the accuracy and reliability of the simulation results based on statistical indicators was carried out. The root-mean-square deviation of the predicted basicity of the CaO/SiO2 slag from that measured at taps is 0.023, the prediction reliability is 92 %, which indicates a satisfactory agreement between the predicted and actual values of the content of individual components in the slag. The information modeling system developed on the basis of the presented algorithm is integrated into the information system of the blast furnace shop of PJSC Magnitogorsk Iron and Steel Works.

The Digital Twin: A Potential Solution for the Personalized Diagnosis and Treatment of Musculoskeletal System Diseases.

Due to the high prevalence and rates of disability associated with musculoskeletal system diseases, more thorough research into diagnosis, pathogenesis, and treatments is required. One of the key contributors to the emergence of diseases of the musculoskeletal system is thought to be changes in the biomechanics of the human musculoskeletal system. However, there are some defects concerning personal analysis or dynamic responses in current biomechanical research methodologies. Digital twin (DT) was initially an engineering concept that reflected the mirror image of a physical entity. With the application of medical image analysis and artificial intelligence (AI), it entered our lives and showed its potential to be further applied in the medical field. Consequently, we believe that DT can take a step towards personalized healthcare by guiding the design of industrial personalized healthcare systems. In this perspective article, we discuss the limitations of traditional biomechanical methods and the initial exploration of DT in musculoskeletal system diseases. We provide a new opinion that DT could be an effective solution for musculoskeletal system diseases in the future, which will help us analyze the real-time biomechanical properties of the musculoskeletal system and achieve personalized medicine.

ROV State Estimation Using Mixture of Gaussian Based on Expectation-Maximization Cubature Particle Filter

The underwater motion of the ROV is affected by various environmental factors, such as wind, waves, and currents. The complex relationship between these disturbance variables results in non-Gaussian noise distribution, which cannot be handled by the classical Kalman filter. For the accurate and real-time observation of ROV climbing, and, at the same time, to reduce the influence of the uncertainty of the noise distribution, the ROV state filter is designed based on the mixture of Gaussian model theory with the expectation-maximization cubature particle filter (EM-MOGCPF). The EM-MOGCPF considers different sensor measurement noises, and the addition of mixture of Gaussian (MOG) improves the fineness and real-time properties, while the expectation-maximization (EM) reduces the complexity of the algorithm. To estimate the ROV xyz-axis and yaw angular states, we establish a four-degree-of-freedom (4-DOF) ROV kinetics model, which uses a simulation platform for multiple sea state degrees. The results show that the EM-MOGCPF effectively improves the estimation accuracy and exhibits strong adaptability to nonlinear and non-Gaussian environments. We believe that this algorithm holds promise in solving the state estimation challenge in these difficult environments.

KEMAMPUAN PENGENDALIAN JUMLAH BAKTERI PADA TANGAN DAN STABILITAS FISIK GEL HAND SANITIZER EKSTRAK ETANOL 96% KULIT BUAH PISANG AMBON (Musa parasidiaca)

The purpose of this study was to determine the physical stability of hand sanitizer gel 96% ethanol extract of Ambon banana peel (Musa paradisiaca) and the ability to control the number of bacteria on the hands. The formulation is carbopol 940, HPMC, TEA, Methylparaben, and Ambon banana peel extract (Musa paradisiaca). Tests for hand sanitizer gel preparations include organoleptic tests, dispersibility, homogeneity, pH, viscosity, skin irritation, and controlling the number of bacteria on the hands. The results of the organoleptic physical stability test on day 0 and day 30 affected texture and color but did not affect aroma. The dispersion test on day 0 was 6.53 and on day 30 it was 6.26. The homogeneity test on day 0 and day 30 is homogeneous. The pH test on day 0 was 5.96 and on day 30 it was 5.83. The viscosity test on day 0 is 2,900 and on day 30 is 2,900. Test the percentage of the number of bacteria on the hands before and after day 0 the results of R1 were 6,4% R2 75% R3 87,8%. And on day 30 the result is R1 91,4% R2 76,19% R3 97,05%. Based on the results, it can be concluded that the 30-day real-time physical properties stability test met the requirements of the gel preparation. Bacterial control test on the respondent's hands before and after using hand sanitizer gel of Ambon banana peel extract was able to reduce the percentage of bacteria on each respondent's hand.

Data-Driven Framework for Real-time Rheological Properties Prediction of Flat Rheology Synthetic Oil-Based Drilling Fluids.

Appropriate mud properties enhance drilling efficiency and decision quality to avoid incidents. The detailed mud properties are mainly measured in laboratories and are usually measured twice a day in the field and take a long time. This prevents real-time mud performance optimization and adversely affects proactive actions. As a result, it is critical to evaluate mud properties while drilling to capture mud flow dynamics. Unlike other mud properties, mud density (MD) and Marsh funnel viscosity (MFV) are frequently evaluated every 15-20 min in the field. The goal of this study is to predict the rheological properties of flat rheology synthetic oil-based mud (SOBM) in real time using machine learning (ML) techniques such as random forest (RF) and decision tree (DT). A proposed approach is followed to first predict the viscometer readings at 300 and 600 RPM (R 600 and R 300) and then estimate the other mud properties using the existing equations in the literature. A set of data contained MD, MFV, and viscometer readings (R 300 and R 600) for different samples from the same mud type. The mud samples were collected after going through a shale shaker. MD and MFV are measured by a mud balance and a Marsh funnel, respectively, while rheology is evaluated using a viscometer. The data were randomly split into training, testing, and validation data sets. The ML models' performance was evaluated through average absolute percentage error (AAPE) and correlation coefficient (R). The proposed models predicted the viscometer readings as a middle stage with a low AAPE that did not exceed 4.5% for both models. The suggested models forecasted the rheological properties with a good degree of accuracy, with an AAPE being less than 7% for most of the parameters. The proposed models can save costs and time since there is no need to include additional tools in the rig location. Furthermore, these models will significantly aid in avoiding serious problems and achieving better rig hydraulics and hole cleaning, which in turn will technically and economically enhance drilling operations.

Mesoporous silica-modified upconversion biosensor coupled with real-time ion release properties for ultrasensitive detection of Staphylococcus aureus in meat

Staphylococcus aureus ( S. aureus ) is a pathogenic bacterium that can cause serious contamination in meat. Herein, a novel fluorescence biosensor was designed for the rapid detection of S. aureus based on mesoporous silica-modified upconversion nanoparticles (UCNPs-mSiO 2 ). Particularly, the mesoporous silica (MS) was introduced to empower the biosensor with hydrophilic and ion loading capabilities. The results showed that the introduction of S. aureus in the system led to the discharge of Fe 2+ ions by binding to the molecular gating (aptamer) of mesoporous channels. The oxidation of Fe 2+ to Fe 3+ in the presence of hydrogen peroxide, resulted in quenching of the fluorescence of UCNPs. Remarkably, the prepared biosensor achieved low detection limits of 25 CFU/mL and a wide detection range of 63–6.3 × 10 6 CFU/mL. The recovery of S. aureus in meat was 91–93% (pork) and 96–105% (beef), which exhibited insignificant difference compared with the traditional plate counting method. Consequently, with its advantages of sensitivity and speed, the developed biosensor can be a promising platform for S. aureus detection in meat. • A new biosensor for the detection of S. aureus was developed. • Mesoporous silica coated UCNPs were synthesized for ion real-time release. • Fe 2+ was released from the mesopores structure and oxidized to Fe 3+ by H 2 O 2 . • Fe 3+ quenched the fluorescence in the sensor system via photoinduced electron transfer. • The biosensor was successfully applied for the S. aureus detection in real samples.

Review of Intelligence for Additive and Subtractive Manufacturing: Current Status and Future Prospects.

Additive manufacturing (AM), an enabler of Industry 4.0, recently opened limitless possibilities in various sectors covering personal, industrial, medical, aviation and even extra-terrestrial applications. Although significant research thrust is prevalent on this topic, a detailed review covering the impact, status, and prospects of artificial intelligence (AI) in the manufacturing sector has been ignored in the literature. Therefore, this review provides comprehensive information on smart mechanisms and systems emphasizing additive, subtractive and/or hybrid manufacturing processes in a collaborative, predictive, decisive, and intelligent environment. Relevant electronic databases were searched, and 248 articles were selected for qualitative synthesis. Our review suggests that significant improvements are required in connectivity, data sensing, and collection to enhance both subtractive and additive technologies, though the pervasive use of AI by machines and software helps to automate processes. An intelligent system is highly recommended in both conventional and non-conventional subtractive manufacturing (SM) methods to monitor and inspect the workpiece conditions for defect detection and to control the machining strategies in response to instantaneous output. Similarly, AM product quality can be improved through the online monitoring of melt pool and defect formation using suitable sensing devices followed by process control using machine learning (ML) algorithms. Challenges in implementing intelligent additive and subtractive manufacturing systems are also discussed in the article. The challenges comprise difficulty in self-optimizing CNC systems considering real-time material property and tool condition, defect detections by in-situ AM process monitoring, issues of overfitting and underfitting data in ML models and expensive and complicated set-ups in hybrid manufacturing processes.

Flame-Retardant and Form-Stable Delignified Wood-Based Phase Change Composites with Superior Energy Storage Density and Reversible Thermochromic Properties for Visual Thermoregulation

The development of form-stable phase change materials (PCMs) with flame retardancy and the visual thermal storage process is crucial for their application in building energy conservation. Herein, an active phosphorus/ammonium-containing non-formaldehyde flame retardant (APA) was synthesized based on the natural compound phytic acid. Then, wood-based form-stable PCM composites (PTPCMs) with high energy storage density, excellent flame retardancy, and real-time and visual reversible thermochromic properties were successfully fabricated by impregnating the thermochromic compound into the APA-grafted delignified wood. The delignified wood well supports the solid–liquid PCMs and avoids their liquid leakage during phase transition due to the high surface tension and strong capillary effect. The differential scanning calorimetry (DSC) results showed that the PTPCMs possessed high thermal energy storage density (165.3–198.6 J/g) and reliable thermal stability. With the concentration of the flame-retardant APA increased, the peak heat release rate (pHRR) and total heat release (THR) of PTPCMs reduced noticeably, demonstrating the enhanced flame retardancy of PTPCMs. Moreover, PTPCM composites had good thermoregulation properties and the visualization of the phase transition process was made possible by the reversible thermochromic properties. In summary, the novel PTPCMs show tremendous application potential for efficient building energy conservation.

Effect of Fibrillization pH on Gelation Viscoelasticity and Properties of Biofabricated Dense Collagen Matrices via Gel Aspiration-Ejection.

Reconstituted hydrogels based on the self-assembly of acid-solubilized collagen molecules have been extensively used as in vitro models and precursors in biofabrication processes. This study investigated the effect of fibrillization pH-ranging from 4 to 11-on real-time rheological property changes during the gelation of collagen hydrogels and its interplay with the properties of subsequently biofabricated dense collagen matrices generated via automated gel aspiration-ejection (GAE). A contactless, nondestructive technique was used to characterize the temporal progression in shear storage modulus (G', or stiffness) during collagen gelation. There was a relative increase in G' of the hydrogels from 36 to 900 Pa with an increase in gelation pH. Automated GAE, which simultaneously imparts collagen fibrillar compaction and alignment, was then applied to these precursor collagen hydrogels to biofabricate native extracellular matrix-like densified gels. In line with viscoelastic properties, only hydrogels fibrillized in the 6.5 < pH ≤ 10 range could be densified via GAE. There was an increase in both fibrillar density and alignment in the GAE-derived matrices with an increase in gelation pH. These factors, combined with a higher G' in the alkaline precursor hydrogels, led to a significant increase in the micro-compressive modulus of GAE-densified gels of pH 9 and 10. Furthermore, NIH/3T3 fibroblast-seeded GAE-derived matrices densified from gels fibrillized in the pH range of 7 to 10 exhibited low cell mortality with >80% viability. It is anticipated that the results of this study can be potentially applicable to other hydrogel systems, as well as biofabrication techniques involving needles or nozzles, such as injection and bioprinting.

Interactive Quantum Chemistry Enabled by Machine Learning, Graphical Processing Units, and Cloud Computing.

Modern quantum chemistry algorithms are increasingly able to accurately predict molecular properties that are useful for chemists in research and education. Despite this progress, performing such calculations is currently unattainable to the wider chemistry community, as they often require domain expertise, computer programming skills, and powerful computer hardware. In this review, we outline methods to eliminate these barriers using cutting-edge technologies. We discuss the ingredients needed to create accessible platforms that can compute quantum chemistry properties in real time, including graphical processing units-accelerated quantum chemistry in the cloud, artificial intelligence-driven natural molecule input methods, and extended reality visualization. We end by highlighting a series of exciting applications that assemble these components to create uniquely interactive platforms for computing and visualizing spectra, 3D structures, molecular orbitals, and many other chemical properties. Expected final online publication date for the Annual Review of Physical Chemistry, Volume 74 is April 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Controllable Phase Transition Properties in VO2 Films via Metal-Ion Intercalation.

VO2 has shown great promise for sensors, smart windows, and energy storage devices, because of its drastic semiconductor-to-metal transition (SMT) near 340 K coupled with a structural transition. To push its application toward room-temperature, effective transition temperature (Tc) tuning in VO2 is desired. In this study, tailorable SMT characteristics in VO2 films have been achieved by the electrochemical intercalation of foreign ions (e.g., Li ions). By controlling the relative potential with respect to Li/Li+ during the intercalation process, Tc of VO2 can be effectively and systematically tuned in the window from 326.7 to 340.8 K. The effective Tc tuning could be attributed to the observed strain and lattice distortion and the change of the charge carrier density in VO2 introduced by the intercalation process. This demonstration opens up a new approach in tuning the VO2 phase transition toward room-temperature device applications and enables future real-time phase change property tuning.

Analytical investigation of nano-bio interfacial protein mediation for fibroblast adhesion on hydroxyapatite nanoparticles.

A quartz crystal microbalance with dissipation (QCM-D) analysis was used to investigate fetal bovine serum (FBS) protein preadsorption on a hydroxyapatite (HAp) surface and the subsequent adhesion process of fibroblasts as compared with the case of oxidized poly(styrene) (PSox). The results showed that the preadsorption of FBS proteins on HAp promoted the subsequent initial cell adhesion ability. Moreover, the measured frequency (Δf) and dissipation shift (ΔD) curves, ΔD-Δf plots and viscoelastic analysis were used to study the initial cell adhesion process in real time. It was suggested that FBS-HAp showed sensitive changes in mass and viscoelasticity as compared with FBS-PSox, which realized the in situ reflection of the cell adhesion state, and the interfacial reactions between the cells and FBS-HAp surfaces such as dehydration and binding occurred to promote the initial cell adhesion and spreading. The viscoelastic analysis of the interface layer showed that the adhered cells on FBS-HAp could secrete some viscous substances such as extracellular matrix (ECM) proteins at the interfaces to provide good adhesion behaviors, and the Voigt-based viscoelastic model could clearly reveal the cellular interfacial viscoelasticity depending on the substrate surface. In addition, the morphology of cells was observed by confocal laser scanning microscopy (CLSM) and atomic force microscopy (AFM), and it was found that the pseudopodia were more uniformly stretched on FBS-HAp than on FBS-PSox. Furthermore, the state of the interfacial protein layer was analyzed by localized Fourier-transform infrared (FT-IR) spectroscopy and fluorescence microscopy (FLM), and it was indicated that the type of substrate affects the formation state of ECM proteins, resulting in changes in cell adhesion properties and morphology. The abundant formation of connective proteins (i.e., collagen type I) on FBS-HAp promoted subsequent pseudopodia formation and cell spreading. Therefore, the initial adhesion properties of fibroblasts on the FBS-HAp surface were systematically studied, which is of great importance for understanding the interfacial interaction between biomaterials and cells, and has great application value in biomedical fields.

Application of seismic sensors on measurement while drilling for real-time rock property detection

Rock type and geological structures are often characterized with geotechnical and geological data gathered from boreholes that are drilled considerable distances apart. Such characterization may not identify localized variations associated with rock mass. The continual acquisition of rock data while drilling on a local scale has a potential to provide confidence in detecting rock types and interpreting rock properties, and depending on the application, may inform downstream operations.Drill returned parameters, such as, rotary speed, penetration rate, sound, torque, vibration, can provide insights into rock geotechnical properties. In the past, most of the researches in this area are limited to the use of conventional drill parameters such as penetration rate, rotary speed, torque and weight on bit. Noisy signals from drilling operations often pose a major challenge from using these conventional parameters to reliably interpret rock properties. To overcome this issue, in addition to the conventional drill parameters, the feasibility and application of seismic sensors on identifying relative rock properties have been explored in the current research. The interaction between the drill bit and rock mass being drilled generates seismic signals, which vary with different rock conditions and drilling operations. These seismic signals can be interpreted to detect rock types, rock properties, rock-ore interfaces and geological structures on a local scale.Laboratory experiments with a rotary drilling machine on synthesized rock samples with and without interfaces have been conducted. Preliminary laboratory results show clear seismic signatures when drilling through the rocks with various strengths and interfaces. The results obtained using the conventional drill parameters and seismic data have also been compared in this paper.

Blockchain Protocol-Based Predictive Secure Control for Networked Systems

Networked control systems (NCSs) are widely used in practical applications because of their flexibility in deployment. However, due to the dependence on the communication network, NCSs could be vulnerable to malicious cyberattacks. To address this problem, a novel blockchain technology-assisted networked predictive secure control approach is presented for the first time in this article. First, the introduction of blockchain technology brings a significant boost to the inherent resilience of the NCS in an active manner without relying on any prior knowledge of the system or potential attacks. However, blockchain technology would induce time delays unfavorable to the NCS, which could result in the low real-time performance of the control system. Subsequently, a networked Kalman filter-based predictive control is specially designed to compensate for the low real-time property of blockchain technology. A detailed analysis of the security and stability of the closed-loop NCS with the developed networked predictive secure controller is also presented, while sufficient conditions for the closed-loop system to be simultaneously stable and safe in a probabilistic sense are given. Finally, to verify the performance of the proposed approach in terms of practicality, an experimental prototype of a photovoltaic (PV)-based power generation system subjected to random cyber-attacks is built for voltage regulation.

Extracellular-matrix-mimicked 3D nanofiber and hydrogel interpenetrated wound dressing with a dynamic autoimmune-derived healing regulation ability based on wound exudate

Dynamic regulation of wound physiological signals is the basis of wound healing. Conventional biomaterials delivering growth factors to drive wound healing leads to the passive repair of soft tissues because of the mismatch of wound healing stages. Meanwhile, the bioactivity of wound exudate is often restricted by oxidation and bacterial contamination. Herein, an extracellular matrix mimicked nanofiber/hydrogel interpenetrated network (NFHIN) was constructed with a 3D nanofibrous framework for cell immigration, and interfiled aerogel containing cross-linked hyaluronic acid and hyperbranched polyamidoamine to balance the wound microenvironment. The aerogel can collect wound exudate and transform into a polycationic hydrogel with contact-killing effects even against intracellular pathogens (bactericidal rate > 99.9% in 30 min) and real-time scavenging property of reactive oxygen species. After co-culturing with the NFHIN, the bioactivity of fibroblast in the ex vivo blister fluid was improved by 389.69%. The NFHIN showed sustainable exudate management with moisture–vapor transferring rate (6000 g m−2 ×24 h), equilibrium liquid content (75.3%), Young’s modulus (115.1 ± 7 kPa), and anti-tearing behavior similar to human skin. The NFHIN can collect and activate wound exudate, turning it from a clinical problem to an autoimmune-derived wound regulation system, showing potential for wound care in critical skin diseases.

Low Latency and High Data Rate (LLHD) Scheduler: A Multipath TCP Scheduler for Dynamic and Heterogeneous Networks.

The scheduler is a crucial component of the multipath transmission control protocol (MPTCP) that dictates the path that a data packet takes. Schedulers are in charge of delivering data packets in the right order to prevent delays caused by head-of-line blocking. The modern Internet is a complicated network whose characteristics change in real-time. MPTCP schedulers are supposed to understand the real-time properties of the underlying network, such as latency, path loss, and capacity, in order to make appropriate scheduling decisions. However, the present scheduler does not take into account all of these characteristics together, resulting in lower performance. We present the low latency and high data rate (LLHD) scheduler, which successfully makes scheduling decisions based on real-time information on latency, path loss, and capacity, and achieves around 25% higher throughput and 45% lower data transmission delay than Linux's default MPTCP scheduler.

A Robust Extraction Approach of Auditory Brainstem Response Using Adaptive Kalman Filtering Method.

The Auditory brainstem response (ABR) can provide valuable information on the function of the auditory pathway. However, the ABR signal has a very small amplitude, and it is easily submerged in different background noises with large amplitude. Conventional ABR extraction methods such as time-domain averaging (TDA) and Kalman filter (KF) were greatly affected by noise intensity, and the result relies on the empirical settings of parameters. ABR extraction method that can automatically adjust parameters to adapt different background noises was needed. An adaptive Kalman filtering (AKF) based ABR signal extraction method was proposed, in which two recursive rules were introduced to constantly update the parameters according to the real-time noise properties. It was used for ABR extraction from recordings in noises with different orders of larger magnitude. The AKF method demonstrated the best performance in obtaining reliable ABR waveform morphologies in the presence of large EMG noises compared with traditional methods of TDA or KF. It could extract satisfactory ABR signal with fewer trials of acoustic stimulus repetition, even from noise 10000 times larger than ABR signal. The AKF results also showed smaller absolute errors and higher correlation coefficients with the target ABR signal when different types (gum chewing, mouth opening and milk drinking) or levels of noises were introduced. The proposed AKF method is a great candidate to increase the robustness of current ABR measurements. It could provide reduced testing time and relaxed recording conditions for ABR and other evoked potentials extraction.

Next-Cycle Optimal Dilute Combustion Control via Online Learning of Cycle-to-Cycle Variability Using Kernel Density Estimators

Dilute combustion using exhaust gas recirculation (EGR) presents a cost-effective method for increasing the efficiency of spark-ignition (SI) engines. However, the maximum amount of EGR that can be used at a given condition is limited by a rapid increment of cycle-to-cycle variability (CCV). This study describes a methodology to design a model-based stochastic optimal controller to adjust the cycle-to-cycle fuel injection quantity in order to reduce CCV and further extend the dilute limit. Given the complexity and chaotic nature of combustion events, the controller was enhanced with online learning in order to identify the statistical properties of combustion efficiency, which are needed to generate predictions for next-cycle events. This study showed that a kernel density estimator (KDE) can be used to learn the combustion properties in real time and can be incorporated into the feedback policy in order to calculate the optimal control command. Experimental results suggested that the dilute limit can be extended from 18.5% to 21% EGR fraction at an operating condition relevant for highway cruising. Additionally, the proposed controller can achieve a large CCV reduction with less fuel enrichment compared to previous methods, overall contributing to an increase in 0.2% indicated fuel conversion efficiency.